Assessing coverage of the Monitoring Framework of the Kunming-Montreal Global Biodiversity Framework and opportunities to fill gaps
Bibliographic record
Abstract
Abstract The Kunming-Montreal Global Biodiversity Framework (GBF) is the most ambitious agreement on biodiversity conservation and sustainable use to date. It calls for a whole-of government and whole-of-society approach to halt and reverse biodiversity loss worldwide. The Monitoring Framework of the GBF lays out how Parties to the Convention on Biological Diversity (CBD) are expected to report their progress. A CBD expert group provided guidance on its implementation, including a gap analysis to identify the strengths and limitations of the indicators in the Monitoring Framework. We present the results of the gap analysis, highlight where more work is needed and provide recommendations on implementing and improving monitoring to allow effective and comprehensive tracking of the GBF’s ambition. We find that with the headline and binary indicators, which Parties are required to use, the Monitoring Framework fully covers 19% of the GBF’s ambition and partially covers an additional 40%. Including disaggregations of the headline indicators improves coverage to 22% fully and an additional 41% partially. Adding optional (component and complementary) indicators brings full coverage to 29% with an additional 47% partial coverage. No indicators are available for 12% of the GBF. In practice, the coverage of the Monitoring Framework will depend on which indicators (headline and binary as well as component and complementary) and disaggregations are used by countries. Disaggregations are particularly relevant to monitor the cross-cutting considerations defined under section C. Substantial investment is required to collect the necessary data to compute indicators, infer change, and effectively monitor progress. We highlight important next steps to progressively improve the efficacy of the Monitoring Framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".